Producing chemical formulations with cognitive computing

ABSTRACT

A cognitive computing system for producing chemical formulations, in some embodiments, comprises: neurosynaptic processing logic; and one or more information repositories accessible to the neurosynaptic processing logic, said one or more repositories storing resources, wherein the neurosynaptic processing logic determines a first chemical formulation to achieve a target and to satisfy one or more constraints, produces and tests said first chemical formulation, and analyzes the results of the test using said resources to determine a second chemical formulation, wherein the second chemical formulation more closely achieves the target and satisfies the one or more constraints than the first chemical formulation.

BACKGROUND

Computer scientists and engineers have long tried to create computersthat mimic the mammalian brain. Such efforts have met with limitedsuccess. While the brain contains a vast, complex and efficient networkof neurons that operate in parallel and communicate with each other viadendrites, axons and synapses, virtually all computers to date employthe traditional von Neumann architecture and thus contain some variationof a basic set of components (e.g., a central processing unit,registers, a memory to store data and instructions, external massstorage, and input/output devices). Due at least in part to thisrelatively simple architecture, von Neumann computers are adept atperforming calculations and following specific, deterministicinstructions, but—in contrast to the biological brain—they are generallyinefficient; they adapt poorly to new, unfamiliar and probabilisticsituations; and they are unable to learn, think, and handle data that isvague, noisy, or otherwise imprecise. These shortcomings substantiallylimit the traditional von Neumann computer's ability to make meaningfulcontributions in industries that produce chemical formulations—forexample, the oil and gas industry, the petrochemical industry and thepharmaceutical industry.

In such industries there is a heavily reliance on human effort, whichexacerbates the inefficiencies introduced by the von Neumann computer.Typically, in a chemical development environment (e.g., a research anddevelopment laboratory), chemists and engineers perform a set ofexperiments involving various chemical formulations and reactions in aneffort to produce a chemical formulation that meets one or moreperformance targets while honoring one or more predetermined constraints(e.g., budget limitations). This process is typically iterative, withnew information obtained from each round of experiments being used insubsequent experiments to further optimize the chemical beingformulated. Such processes are labor-intensive and inefficient, oftenrequiring hundreds or even thousands of man-hours to produce a chemicalformulation with the desired characteristics.

BRIEF DESCRIPTION OF THE DRAWINGS

Accordingly, there are disclosed in the drawings and in the followingdescription cognitive computing systems and methods for producing andenhancing chemical formulations. In the drawings:

FIG. 1A is an illustration of a pair of biological neurons communicatingvia a synapse.

FIG. 1B is a mathematical representation of an electronic neuron.

FIG. 1C is a schematic diagram of a neurosynaptic tile for use in acognitive computer.

FIG. 1D is a schematic diagram of a circuit that embodies an electronicsynapse.

FIG. 1E is a schematic diagram of an electronic neuron.

FIG. 1F is a block diagram of an electronic neuron spiking logic.

FIG. 2 is a schematic diagram of a neurosynaptic core for use in acognitive computer.

FIG. 3 is a schematic diagram of a multi-core neurosynaptic chip for usein a cognitive computer.

FIG. 4 is a detailed schematic diagram of a dual-core neurosynaptic chipfor use in a cognitive computer.

FIGS. 5 and 6 are conceptual diagrams of scalable corelets used forprogramming neurosynaptic processing logic.

FIG. 7 is a block diagram of a cognitive computing system that hasaccess to multiple information repositories.

FIG. 8 is a block diagram of a cognitive computing system controlling achemical formulation system and a field implementation system.

FIG. 9 is a flow diagram of a method used to produce and enhancechemical formulations.

It should be understood, however, that the specific embodiments given inthe drawings and detailed description thereto do not limit thedisclosure. On the contrary, they provide the foundation for one ofordinary skill to discern the alternative forms, equivalents, andmodifications that are encompassed together with one or more of thegiven embodiments in the scope of the appended claims.

DETAILED DESCRIPTION

Disclosed herein are methods and systems for producing and enhancingchemical formulations using cognitive computers. Cognitivecomputers—also known by numerous similar terms, including artificialneural networks, neuromorphic and synaptronic systems, and, in thisdisclosure, neurosynaptic systems—are modeled after the mammalian brain.In contrast to traditional von Neumann architectures, neurosynapticsystems include extensive networks of electronic neurons and coresoperating in parallel with each other. These electronic neurons functionin a manner similar to that in which biological neurons function, andthey couple to electronic dendrites, axons and synapses that functionlike biological dendrites, axons and synapses. By modeling processinglogic after the biological brain in this manner, cognitivecomputers—unlike von Neumann machines—are able to support complexcognitive algorithms that replicate the numerous advantages of thebiological brain, such as adaptability to ambiguous, unpredictable andconstantly changing situations and settings; the ability to understandcontext (e.g., meaning, time, location, tasks, goals); and the abilityto learn new concepts.

Key among these advantages is the ability to learn, because learningfundamentally drives the cognitive computer's behavior. In the cognitivecomputer—just as with biological neural networks—learning (e.g., Hebbianlearning) occurs due to changes in the electronic neuron and synapses asa result of prior experiences (e.g., a training session with a humanuser) or new information. These changes, described below, affect thecognitive computer's future behavior. In a simple example, a cognitivecomputer robot with no prior experience or software instructions withrespect to coffee preparation can be introduced to a kitchen, shown whata bag of ground coffee beans looks like, and shown how to use a coffeemachine. After the robot is trained, it will be able to locate materialsand make the cup of coffee on its own, without human assistance.Alternatively, the cognitive computer robot may simply be asked to makea cup of coffee without being trained to do so. The computer may accessinformation repositories via a network connection (e.g., the Internet)and learn what a cup is, what ground coffee beans are, what they looklike and where they are typically found, and how to use a coffeemachine—for example, by means of a YOUTUBE® video. A cognitive computerrobot that has learned to make coffee in other settings in the past mayengage in a conversation with the user to ask a series of specificquestions, such as to inquire about the locations of a mug, groundcoffee beans, water, the coffee machine, and whether the user likessugar and cream with his coffee. If, while preparing the coffee, a wetcoffee mug slips from the robot's hand and falls to the floor, the robotmay infer that a wet mug is susceptible to slipping and it may grasp awet mug a different way the next time it brews a cup of coffee.

The marriage between neurosynaptic architecture and cognitive algorithmsrepresents the next step beyond artificial intelligence and can proveespecially useful in the oil and gas industry. This disclosure describesthe use of neurosynaptic technology (and associated cognitivealgorithms) to intelligently design, produce and enhance chemicalformulations in various contexts—for example, in the oil and gas,petrochemical and pharmaceutical industries. In particular, a cognitivecomputer implementing neurosynaptic technology leverages access tovarious resources—such as books, studies, papers, databases, tradejournals, chemistry models and formulas, presentations, and the like—toiteratively determine a chemical formulation that is most likely toachieve one or more specified targets while satisfying one or morespecified constraints. The cognitive computer performs these actionsintelligently, probabilistically and with minimal or no human assistanceusing its neurosynaptic architecture and cognitive algorithms.

More specifically, a cognitive computer engaged in producing chemicalformulations is provided with information about a desired target (or, insome embodiments, multiple desired targets). The target is typicallydescribed in terms of the desired function of the chemical formulation.For example and without limitation, the target may be a drilling fluidthat reduces non-productive time (NPT) below a given threshold in agiven well. The cognitive computer would be provided with this targetinformation in as much detail as possible or practical. In someembodiments, the cognitive computer obtains some of this information onits own through the various resources to which it has access. Thecognitive computer may also be provided with one or more constraintsthat must be satisfied by a particular chemical formulation thatpurports to meet the target. These constraints vary widely and mayinclude, without limitation, chemical properties (e.g., corrosionresistance, pH, reactivity, surface tension, heat of combustion,enthalpy of formation, toxicity, chemical stability in a givenenvironment, flammability, oxidation state(s)); rheology; time-dependentrheology; mixture of compositions; salinity; turbidity; solubility;filtration characteristics; chemical/geometric structure; cost tomanufacture; time to manufacture, and the like. The specific types ofconstraints provided to the cognitive computer may vary depending on theparticular application or industry in question. The constraints providedfor drilling fluid, for instance, may differ from the constraintsprovided for a pharmaceutical compound.

The cognitive computer uses the target(s) and constraint(s) to identifyresources that may be helpful in designing a first chemical formulationfor production and in determining how best to produce the first chemicalformulation. For example, given a set of target(s) and constraint(s),the cognitive computer may access textbooks, technical journals,chemistry databases, the periodic table of elements, chemical materialspricing guides, and numerous other relevant resources to design achemical formulation that would achieve the target while honoring theconstraints. After designing the first chemical formulation, thecognitive computer produces the formulation. Specifically, the cognitivecomputer couples to or comprises a hardware control system. The hardwarecontrol system controls the various components of a chemical productionsystem. For instance, the cognitive computer—via the hardware controlsystem—controls chemical and material storage queues, mixing systems,testing systems, conditioning systems, cleaning and disposal systems,etc. In this way, the cognitive computer causes the hardware controlsystem to use these various components of the chemical production systemto produce and test the first chemical formulation. The specificchemical and material combinations, preparations and tests that areperformed by the chemical production system are determined by thecognitive computer as it analyzes the target(s), constraint(s) andresources to which it has access. Alternatively or in addition, thecognitive computer may have received training (e.g., from a human orother cognitive computer) that aids the cognitive computer inidentifying and producing the first chemical formulation. Similarly, thecognitive computer may have learned from similar experiences in the pastand it may use these experiences to help design and produce the firstchemical formulation.

Once the cognitive computer produces the first chemical formulation, itis tested to determine one or more parameters associated with thatformulation. The specific parameters tested vary and the scope ofdisclosure is not limited to any particular set of parameters to betested. Based on the test results, the cognitive computer may take anynumber of actions. For example, in some embodiments, the cognitivecomputer may use the results to refine the chemical formulation, thusdetermining a second chemical formulation that may be produced. In suchembodiments, the cognitive computer causes the chemical productionsystem to produce the second chemical formulation. The second chemicalformulation, in most cases, will be closer to achieving the target(s)and/or desired constraint(s) than the first chemical formulation, andthis will generally be true for each successive iteration. Thisiterative process may be performed any suitable number of times. Inother embodiments, the cognitive computer develops a fieldimplementation recommendation—for instance, in the case of drillingfluid, a recommendation as to precisely how the first chemicalformulation (or a modified version thereof) should be implemented in adrilling operation. The cognitive computer may unilaterally or uponcommand execute the field implementation recommendation—in the foregoingexample, by adjusting the dosing systems for the drilling fluid in aparticular drilling operation. The application in which the fieldimplementation recommendation is implemented may then be tested asappropriate or desired, and the results of such testing may be used tofurther refine the first chemical formulation (or the modified versionthereof that was used and tested in the field). In still otherembodiments, both of the foregoing embodiments' processes may beperformed.

The cognitive computer is able to discuss the foregoing processes,results and recommendations with a human user or other cognitivecomputer. The cognitive computer can develop arguments using itsprobabilistic design to support its recommendations and to respond tointerrogation by other users or cognitive computers. By discussing theexperiments, results and recommendations with human users and/or othercognitive computers, the cognitive computer is able to intelligentlydesign, develop and defend its chemical formulations.

In this way, the cognitive computer not only determines the design ofchemistry experiments, but it also manages the associatedexperimentation (e.g., mixing/reacting, testing, conditioning) as well.Most or all mechanical processes associated with chemical developmentand production are managed by the cognitive computer. The cognitivecomputer is retrospective in that it iteratively evaluates its own priorperformance in light of the target(s), constraint(s) and/or resources toimprove performance in the next iteration of chemical development.

FIG. 1A is an illustration of a pair of biological neurons communicatingvia a synapse. Specifically, neuron 20 includes a nucleus 22, dendrites24, an axon 26 and a synapse 28 by which it communicates with anotherneuron 30. The dendrites 24 serves as inputs to the neuron 20, while theaxon 26 serves as an output from the neuron 20. The synapse 28 is thespace between an axon of neuron 30 and a dendrite 24 of neuron 20, andit enables the neuron 30 to output information to the neuron 20 usingneurotransmitters (e.g., dopamine, norepinephrine). The neuron 20receives input from numerous neurons (not specifically shown) inaddition to the neuron 30. Each of these inputs impacts the neuron 20 indifferent ways. Some of these neurons provide excitatory signals to theneuron 20, while other neurons provide inhibitory signals to the neuron20. Excitatory signals push the membrane potential (i.e., the voltagedifference between the neuron and the space surrounding the neuron,typically about −70 mV) toward a threshold value which, if exceeded,results in an action potential (or “spiking,” which is the transmissionof a pulse) of the neuron 20, and inhibitory signals pull the membranepotential of the neuron 20 away from this threshold. The repeatedexcitation or inhibition the neuron 20 through these different inputpathways results in learning. Stated another way, if a particular inputto a neuron repeatedly and persistently causes that neuron to fire, ametabolic change occurs in the synapse associated with that input axonto reduce the resistance in the synapse. This phenomenon is known as theHebbian learning rule. In a more specific version of Hebbian learning,called spike-timing-dependent plasticity (STDP), repeated presynapticspike arrival a few milliseconds before postsynaptic action potentialsleads to long-term potentiation of that synapse, whereas repeatedpresynaptic spike arrival a few milliseconds after postsynaptic actionpotentials leads to long-term depression of the same synapse. STDP isthus a form of neuroplasticity, in which synaptic changes occur due tochanges in behavior, environment, neural processes, thinking, andemotions.

FIG. 1B is a mathematical representation of an electronic neuron 50 thatmimics the behavior of a biological neuron. Specifically, the electronicneuron 50 includes a nucleus 52 that has multiple inputs I₁, I₂, . . . ,I_(N), and these inputs are associated with weights W₁, W₂, . . . ,W_(N), respectively. The weight associated with an input dictates theimpact that input will have upon the neuron 50 and, more specifically,on the electronic neuron's mathematical equivalent of a biologicalmembrane potential (which, for purposes of this discussion, will stillbe referred to as a membrane potential). The summation of the weightedinputs produces a membrane potential x, which causes a spike 56 if thepotential x exceeds a threshold value T (numeral 54). Similar to Hebbianlearning, repeated and persistent signals from a particular input to theelectronic neuron 50 that causes the neuron to spike results in a shiftin the magnitudes of weights W₁, W₂, . . . , W_(N) to increase theweight associated with that particular input.

FIG. 1C is a schematic diagram of a neurosynaptic tile 100 for use in acognitive computer. The neurosynaptic tile 100 includes a plurality ofelectronic neurons 102 ₁, 102 ₂, . . . , 102 _(N). The tile 100 furtherincludes a plurality of electronic neurons 104 ₁, 104 ₂, . . . , 104_(N). Each of the neurons 104 ₁, 104 ₂, . . . , 104 _(N) couples to anaxon 106 ₁, 106 ₂, . . . , 106 _(N) (generally indicated by numeral106), respectively. Similarly, each of the neurons 102 ₁, 102 ₂, . . . ,102 _(N) couples to a dendrite 108 ₁, 108 ₂, . . . , 108 _(N) (generallyindicated by numeral 108), respectively. The axons 106 and dendrites 108couple to each other in predetermined locations. For example, axon 106 ₁couples to dendrite 108 ₁ at an electronic synapse 110; axon 106 ₂couples to dendrites 108 ₂, 108 _(N) at synapses 112, 116, respectively;and axon 106 _(N) couples to dendrite 108 ₁ at synapse 114. Inoperation, when any of the membrane potentials of the electronic neurons104 ₁, 104 ₂, . . . , 104 _(N) reaches or exceeds a threshold value,that neuron(s) fires on the corresponding axon(s) 106. The dendrites 108to which the firing axons 106 couple receive the spikes and provide themto the neurons 102 ₁, 102 ₂, . . . , 102 _(N).

As explained above with respect to FIG. 1B, an electronic neuron mayascribe different weights to each input provided to that neuron. Thesame is true for the electronic neurons 102 ₁, 102 ₂, . . . , 102 _(N)and 104 ₁, 104 ₂, . . . , 104 _(N). Thus, for example, the dendrite 108₁, which corresponds to electronic neuron 102 ₁, couples to axons 106 ₁,106 _(N) at synapses 110, 114, respectively, and the electronic neuron102 ₁ ascribes different weights to the inputs from dendrites 106 ₁ and106 _(N). If a greater weight is ascribed to dendrite 106 ₁, theexcitatory or inhibitory signal provided by that dendrite receivesgreater consideration toward the calculation of the membrane potentialof the neuron 102 ₁. Similarly, if a greater weight is ascribed todendrite 106 _(N), the excitatory or inhibitory signal provided by thatdendrite receives greater consideration toward the calculation of themembrane potential of the neuron 102 ₁. If the summation of the weightedsignals received from the dendrites 106 ₁ and 106 _(N) exceeds thethreshold of the neuron 102 ₁, the neuron 102 ₁ spikes on its axon (notspecifically shown). In this way—by strengthening some electronicsynapses and weakening others through the adjustment of inputweights—these neurons implement an electronic version of STDP.

FIG. 1D is a schematic diagram of a circuit that embodies an electronicsynapse, such as the electronic synapses 110, 112, 114, 116 shown inFIG. 1C. Specifically, the electronic synapse 120 in FIG. 1D includes anode 122 that couples to an axon, a node 124 that couples to a dendrite,and a memristor 126 to store data. An optional access or control device128 (e.g., a PN diode or field effect transistor (FET) wired as a diode,or some other element with a non-linear voltage-current response) may becoupled in series with the memristor 126 to prevent cross-talk duringcommunication of neuronal spikes on adjacent axons or dendrites and tominimize leakage and power consumption. In some embodiments, a differentmemory element (e.g., static random access memory (SRAM), dynamic randomaccess memory (DRAM), enhanced dynamic random access memory (EDRAM)) isused in lieu of the memristor 126.

FIG. 1E is a schematic diagram of an electronic neuron 130.Specifically, an electronic neuron 130 comprises electronic neuronspiking logic 131 and multiple resistor-capacitor (RC) circuits 132,134. Although only two RC circuits are shown in the electronic neuron130 of FIG. 1E, any suitable number of RC circuits may be used. Each RCcircuit includes a resistor 136 and a capacitor 138 coupled as shown.When an electronic neuron fires (i.e., issues a spike) as a result ofits membrane potential exceeding the neuron's firing threshold, theneuron maintains pre-synaptic and post-synaptic STDP variables. Each ofthese variables is a signal that decays with a relatively long timeconstant that is determined based on the values of the capacitor in adifferent one of the RCs 132, 134. Each of these signals may be sampledby determining the voltage across a corresponding RC circuit capacitorusing, e.g., a current mirror. By sampling each of the variables, thelength of time between the arrival of a pre-synaptic spike and apost-synaptic action potential following the spike arrival can bedetermined, as can the length of time between a post-synaptic actionpotential and a pre-synaptic spike arrival following the actionpotential. As explained above, the lengths of these times are used inSTDP—that is, to effect synaptic potentiation and depression byadjusting synaptic weights, and thus to facilitate neurosynapticlearning.

FIG. 1F is a block diagram of the electronic neuron spiking logic 131 ofFIG. 1E. The logic 131 includes three conceptual components: a synapticcomponent 140, a neuronal core component 142, and a comparator component144. Although FIG. 1F shows only one synaptic component 140, inpractice, a separate synaptic component 140 is used for each synapsefrom which the electronic neuron receives input. Thus, in someembodiments the electronic neuron contains multiple synaptic components140, one for each synapse from which that neuron receives input. Inother embodiments, the synaptic component 140 forms a part of thesynapse itself and not the electronic neuron. In either type ofembodiment, the end result is the same.

Each synaptic component 140 includes an excitatory/inhibitory signalgenerator 146, a weight signal generator 148 associated with thecorresponding synapse, and a pulse generator 150. The pulse generator150 receives a clock signal 152 and a spike input signal 154, as well asa weight signal 151 from the weight signal generator 148. The pulsegenerator 150 uses its inputs to generate a weighted spike signal158—for instance, the spike input signal 154 multiplied by the weightsignal 151. The width of the weighted spike signal pulse reflects themagnitude of the weighted signal, and thus the magnitude that willcontribute to or take away from the membrane potential of the electronicneuron. The weighted signal for the synapse corresponding to thesynaptic component 140 is provided to the core component 142, andsimilar weighted signals are provided from synaptic components 140corresponding to other synapses from which the electronic neuronreceives input. For each weighted signal that the core 142 receives froma synaptic component 140, the core 142 also receives a signal 156 fromthe excitatory/inhibitory signal generator 146 indicating whether theweighted signal 158 is an excitatory (positive) or inhibitory (negative)signal. An excitatory signal pushes the membrane potential of theelectronic neuron toward its action potential threshold, while aninhibitory signal pulls the membrane potential away from the threshold.As explained, the neurosynaptic learning process involves the adjustmentof synaptic weights. Such weights can be adjusted by modifying theweight signal generator 148.

The core component 142 includes a membrane potential counter 160 and aleak-period counter 162. The membrane potential counter receives theweighted signal 158 and the excitatory/inhibitory signal 156, as well asthe clock 152 and a leak signal 164 from the leak-period counter 162.The leak-period counter 162, in turn, receives only clock 152 as aninput. In operation, the membrane potential counter 160 maintains acounter—initially set to zero—that is incremented when excitatory,weighted signals 158 are received from the synaptic component 140 andthat is decremented when inhibitory, weighted signals 158 are receivedfrom the synaptic component 140. When no synapse pulse is applied to thecore component 142, the leak period counter signal 164 causes themembrane potential counter 160 to gradually decrement at apredetermined, suitable rate. This action mimics the leak experienced inbiological neurons during a period in which no excitatory or inhibitorysignals are received by the neuron. The membrane potential counter 160outputs a membrane potential signal 166 that reflects the present valueof the counter 160. This membrane potential signal 166 is provided tothe comparator component 144.

The comparator component 144 includes a threshold signal generator 168and a comparator 170. The threshold generator 168 generates a thresholdsignal 169, which reflects the threshold at which the electronic neuron130 generates a spike signal. The comparator 170 receives this thresholdsignal 169, along with the membrane potential signal 166 and the clock152. If the membrane potential signal 166 reflects a counter value thatis equal to or greater than the threshold signal 169, the comparator 170generates a spike signal 172, which is subsequently output via an axonof the electronic neuron. As numeral 174 indicates, the spike signal isalso provided to the membrane potential counter 160, which, uponreceiving the spike signal, resets itself to zero.

FIG. 2 is a schematic diagram of a neurosynaptic core 200 for use in acognitive computer. The core 200 includes a neurosynaptic tile 100, acontroller 202, a decoder 204, an encoder 206, inputs 208, and outputs210. Spike events generated by electronic neurons generally take theform of data packets. These packets, which may be received from neuronson other cores external to the core 200, are decoded by the decoder 204(e.g., to interpret and remove packet headers) and passed as inputs 208to the neurosynaptic tile 100. Similarly, packets generated by neuronswithin the neurosynaptic tile 100 that are destined for neurons outsidethe core 200 are passed as outputs 210 to the encoder 206 for encoding(e.g., to include a header with a destination address). The controller202 controls the decoder 204 and encoder 206.

FIG. 3 is a schematic diagram of a multi-core neurosynaptic chip 300 foruse in a cognitive computer. The chip 300 includes a plurality ofneurosynaptic cores 200, such as the core 200 described with respect toFIG. 2. The cores 200 couple to each other via electrical connections(e.g., conductive traces). The chip 300 may include any suitable numberof cores—for example, 4,096 or more cores on a single chip, with eachcore containing millions of electronic synapses. The chip 300 alsocontains a plurality of intrachip spike routers 304 that couple to arouting fabric 302. The cores 200 communicate with each other via therouters 304 and the fabric 302, using the aforementioned encapsulated,encoded packets to facilitate routing between cores and specific neuronswithin the cores.

FIG. 4 is a detailed schematic diagram of a dual-core neurosynaptic chip402 for use in a cognitive computer 400. Specifically, a cognitivecomputer may include any suitable number of neurosynaptic chips 402, andeach of these neurosynaptic chips 402 may include any suitable number ofneurosynaptic cores, as previously explained. In the example of to FIG.4, the neurosynaptic chip 402 is a dual-core chip containingneurosynaptic cores 404, 406. The core 404 includes a synapse array 408that includes a plurality of synapses that couple various axons 410 todendrites. In some embodiments, axons 410 receive spikes from neuronsdirectly coupled to the axons 410 and included on the core 404 (notspecifically shown in FIG. 4, but an illustrative embodiment is shown inFIG. 1). In other embodiments, axons 410 are extensions of neuronslocated off of the core 404 (e.g., elsewhere on the chip 402, or on adifferent chip). In embodiments where the axons 410 couple directly toon-core neurons (e.g., as shown in FIG. 1), the spike router 424provides spikes directly to the neurons' dendrites. In embodiments wherethe axons 410 are extensions of off-core neurons, the spike router 424provides spikes from those neurons to the axons 410. Although amultitude of variations of such embodiments are possible, for brevity,FIG. 4 shows only an array of axons 410.

The synapse array 408 also couples to neurons 412. The neurons 412 maybe a single-row, multiple-column array of neurons, or, alternatively,the neurons 412 may be a multiple-row-, multiple-column array ofneurons. In either case, dendrites of the neurons 412 couple to axons410 in the synapse array 408, thus facilitating the transfer of spikesfrom the axons 410 to the neurons 412 via dendrites in the synapse array408. The spike router 424 receives spikes from off-core sources, such asthe core 406 or off-chip neurons. The spike router 424 uses spike packetheaders to route the spikes to the appropriate neurons 412 (or, in someembodiments, on-core neurons directly coupled to axons 410). In eithercase, bus 428 provides data communication between the spike router 424and the core 404. Similarly, neurons 412 output spikes on their axonsand bus 430 provides the spikes to the spike router 424. The core 406 issimilar or identical to the core 404. Specifically, the core 406contains axons 416, neurons 418, and a synapse array 414. The axons 416couple to a spike router 426 via bus 432, and neurons 418 couple to thespike router 426 via bus 434. The functionality of the core 406 issimilar or identical to that of the core 404 and thus is not described.A bus 436 couples the spike routers 424, 426 to facilitate spike routingbetween the cores 404, 406. A bus 438 facilitates the communication ofspikes on and off of the chip 402. The architectures shown in FIGS. 1-4(e.g., the TRUENORTH® architecture by IBM®) are non-limiting; otherarchitectural configurations are contemplated and included within thescope of the disclosure.

Various types of software may be written for use in cognitive computers.One programming methodology is described below, but the scope ofdisclosure is not limited to this particular methodology. Any suitable,known software architecture for programming neurosynaptic processinglogic is contemplated and intended to fall within the scope of thedisclosure. The software architecture described herein entails thecreation and use of programs that are complete specifications ofnetworks of neurosynaptic cores, along with their external inputs andoutputs. As the number of cores grows, creating a program thatcompletely specifies the network of electronic neurons, axons,dendrites, synapses, spike routers, buses, etc. becomes increasinglydifficult. Accordingly, a modular approach may be used, in which anetwork of cores and/or neurons encapsulates multiple sub-networks ofcores and/or neurons; each of the sub-networks encapsulates additionalsub-networks of cores and/or neurons, and so forth. In some embodiments,the CORELET® programming language, library and development environmentby IBM® may be used to develop such modular programs.

FIGS. 5 and 6 are conceptual diagrams illustrating the modular nature ofthe CORELET® programming architecture. FIG. 5 contains three panels. Thefirst panel illustrates a neurosynaptic tile 500 containing a pluralityof neurons 502 and axons 504, similar to the neurosynaptic architectureshown in FIG. 4. As shown, some of the neurons' outputs couple to theaxons' inputs. However, inputs to other axons 504 are received fromoutside the tile 500, as numeral 506 indicates. Similarly, outputs fromother neurons 502 are provided outside of the tile 500, as numeral 508indicates. The second panel in FIG. 5 illustrates the initial step inthe encapsulation of a tile into a corelet—that is, an abstraction thatrepresents a program (for a neurosynaptic processing logic) that onlyexposes external inputs and outputs while encapsulating all otherdetails into a “black box.” Thus, as shown in the second panel, the onlyinputs to the tile 500 are inputs 506 to some of the axons 504, and theonly outputs from the tile 500 are outputs 508 from some of the neurons502. The inputs 506 couple to an input connector 510, and the outputscouple to an output connector 512. The third panel in FIG. 5 shows thecompleted corelet 514, with only the input connector 510 and outputconnector 512 being exposed, and with the remainder of the tile 500having been encapsulated into the corelet 514. The completed corelet 514constitutes a single building block of the CORELET® modulararchitecture; the corelet 514 may be grouped with one or more othercorelets to form a larger corelet; in turn, that larger corelet may begrouped with one or more other larger corelets to form an even largercorelet, and so forth.

FIG. 6 includes three panels illustrating such encapsulation of multiplesub-corelets into a larger corelet. Specifically, the first panelincludes corelets 602 and 604. Corelet 602 includes an input connector606 and output connector 608. The remainder of the contents of thecorelet 602 do not couple to circuitry outside of the corelet 602 andthus are not specifically shown as being coupled to the input connector606 or the output connector 608. Similarly, corelet 604 includes aninput connector 610 and an output connector 612. Certain inputs to andoutputs from the corelets 602, 604 couple to each other, while othersuch inputs and outputs do not (i.e., inputs 607, 609 are not receivedfrom either corelet 602, 604, and outputs 611, 613 are not provided toeither corelet 602 or 604). Thus, as shown in the second and thirdpanels of FIG. 6, when the corelets 602, 604 are grouped into a single,larger corelet 614, only inputs 607, 609 are exposed on the inputconnector 616, and only outputs 611, 613 are exposed on the outputconnector 618. The remaining contents of the corelet 614 areencapsulated. As explained, one purpose of encapsulating neurosynapticprocessing logic into corelets and sub-corelets is to organize theprocessing logic in a modular way that facilitates the creation ofCORELET® programs, since such programs are complete specifications ofnetworks of neurosynaptic cores. Although FIGS. 5 and 6 demonstrate themodular nature of the CORELET® software architecture, the CORELET®syntax itself is known and is not described here. Cognitive computingsoftware systems other than CORELET® also may be used in conjunctionwith the hardware described herein or with any other suitable cognitivecomputing hardware. All such variations and combinations of potentiallyapplicable cognitive computing hardware and software are contemplatedand may be used to implement the oilfield operations enhancementtechniques described herein.

The remainder of this disclosure describes the use of hardware andsoftware cognitive computing technology to facilitate the enhancement ofoilfield operations. As explained above, any suitable cognitivecomputing hardware or software technology may be used to implement suchtechniques. This cognitive computing technology may include none, someor all of the hardware and software architectures described above. Forexample, the oilfield operations enhancement techniques described belowmay be implemented using the CORELET® programming language or any othersoftware language used in conjunctive with cognitive computers. Theforegoing architectural descriptions, however, are non-limiting. Otherhardware and software architectures may be used in lieu of, or tocomplement, any of the foregoing technologies. Any and all suchvariations are included within the scope of the disclosure.

FIG. 7 is a block diagram of a cognitive computing system 700 that hasaccess to multiple information repositories. Specifically, the cognitivecomputing system 700 includes a cognitive computer 702 (i.e., anysuitable computer that includes neurosynaptic processing logic andcognitive algorithm-based software, such as those described above)coupled to an input interface 704, an output interface 706, a networkinterface 708 and one or more local information repositories 712. In atleast some embodiments, the input interface 704 is any suitable inputdevice(s), such as a keyboard, mouse, touch screen, microphone, videocamera, or one or more wearable devices (e.g., augmented reality devicesuch as GOOGLE) GLASS®). Other input devices are contemplated. Theoutput interface 706 may include one or more of a display and an audiooutput device. Other output devices are contemplated. The networkinterface 708 is, for example, a network adapter or other suitableinterface logic that enables communication between the cognitivecomputer 702 and any device not directly coupled to the cognitivecomputer 702. The local information repositories 712 include, withoutlimitation, thumb drives, compact discs, Bluetooth devices, and anyother device that can couple directly to the cognitive computer 702 suchas by universal serial bus (USB) cable or high definition multimediainterface (HDMI) cable.

The cognitive computer 702 communicates with any number of remoteinformation repositories 710 via the network interface 708. The quantityand types of such information repositories 710 may vary widely, and mayinclude, without limitation, other cognitive computers; databases;distributed databases; sources that provide real-time data pertaining tooil and gas operations, such as drilling, fracturing, cementing, orseismic operations; servers; other personal computers; mobile phones andsmart phones; websites and generally any resource(s) available via theInternet, World Wide Web, or a local network connection such as avirtual private network (VPN); cloud-based storage; libraries; andcompany-specific, proprietary, or confidential data. Any other suitablesource of information with which the cognitive computer 702 cancommunicate is included within the scope of disclosure as a potentialinformation repository 710. The software stored on the cognitivecomputer 702 is probabilistic (i.e., non-deterministic) in nature,meaning that its behavior is guided by probabilistic determinations asto possible events that may occur in the drilling environment beinganalyzed.

FIG. 8 is a block diagram of a cognitive computing system controlling achemical production system and a field implementation system. A system800 generally includes a cognitive computer 802, a chemical orderingsystem 804, a chemical production hardware controller 806, a chemicalstorage queue 808, a mixing system 810, a testing system 812, an agerolling system 814, a cool mixing system 816, a static aging system 818,a special testing system 820 and a testing system 822, a disposal system824, and a cleaning system 826. The system 800 further includesinformation repositories 828 comprising various resources and comprisinga knowledge corpus local to the cognitive computer 802, a data reportingand visualization system 830 (e.g., an output interface, such as adisplay panel), and a field implementation system 832 (e.g., a drillingenvironment in which chemical formulations are deployed). The systems804, 808, 810, 812, 814, 816, 818, 820, 822, 824 and 826 maycollectively be referred to as the chemical production system, althoughin some embodiments the chemical production system may not include thesystems 824 and/or 826.

In operation, the cognitive computer 802 receives target and constraintinformation—for instance, from a human user or from another cognitivecomputer. The target information specifies the goal of the chemicalformulation that is to be designed and produced, and the constraintinformation specifies various criteria that the chemical formulationmust meet—for example and without limitation, criteria regarding price,production time, physical properties, chemical properties, and the like.The cognitive computer 802 uses the target and constraint information toidentify helpful resources on the one or more information repositories828. In some embodiments, the cognitive computer 802 may identify suchresources by reviewing the resources it accessed when designing priorchemical formulations and the degree to which they helped, or by usingresource selection algorithms that it received from, e.g., a user orother cognitive computer. The scope of disclosure is not limited tothese specific techniques for identifying helpful resources whendesigning chemical formulations.

Based on the identified resources, the target(s), the constraint(s), andany other information that the cognitive computer 802 may consider(e.g., training sessions and information it has learned on its own frompast experiences), the cognitive computer 802 designs the first chemicalformulation in an attempt to achieve the target(s) while honoring theconstraint(s). The cognitive computer 802 designs the first chemicalformulation (and any subsequent versions of the first chemicalformulation) using the neurosynaptic architecture described above—thatis, probabilistically, intelligently and with minimal or no humanassistance.

After it has designed the first chemical formulation, the cognitivecomputer 802 causes the first chemical formulation to be produced. Toproduce the first chemical formulation, the cognitive computer 802 usesa chemical ordering system 804—such as corporate buyer software—toprocure the chemicals and other materials necessary to produce the firstchemical formulation. The cognitive computer 802 may have been trainedto procure these chemicals and other materials from preferred vendors,and the computer 802 may adjust shipping preferences based on theurgency of its chemical formulation project. The procured chemicals andother materials are subsequently stored in a chemical storage queue 808.

The cognitive computer 802 couples to the hardware controller 806 andcontrols the components 808-826 through the hardware controller 806(i.e., the cognitive computer 802 has partial or complete control of thehardware controller 806). The hardware controller 806 causes the mixingsystem 810 to combine and react the appropriate chemicals and othermaterials from the chemical storage queue 808 as appropriate and inaccordance with the design for the first chemical formulation. Themixing system 810 may contain and/or have access to any number ofhardware and/or software components—e.g., centrifuges; beakers, testtubes and flasks; microscopes; Bunsen burners; burets; tongs; pipets,and the like, as well as robotic arms to perform the experiments. Insome embodiments, the cognitive computer 802 may command one or morehuman assistants to perform certain tasks, but in most or all suchcases, the humans are not involved in designing the chemicalformulations. The mixing performed by the mixing system 810 preferablyis shear-, time- and temperature-controlled in accordance with thespecifications of the cognitive computer 802.

After the mixing system 810 has produced the first chemical formulation,the hardware controller 806 uses the testing system 812 to test thefirst chemical formulation. The testing system 812 may contain anysuitable testing equipment and, in at least some embodiments, sharesequipment with the mixing system 810. The testing system 812 may testthe first chemical formulation to determine any number of parameters,including, but not limited to, rheology, density, filtration, sag, pH,conductivity, time-dependent rheology; salinity; relative mass of one ormore components (water, oil, etc.); emulsion stability; dielectricconstants, etc. The results of this testing are provided to thecognitive computer 802 and are stored in one or more of the informationrepositories 828.

After the testing is performed, the cognitive computer 802 uses theresults and various resources, as well as its prior training and anyrelevant prior observations it has made, to condition the first chemicalformulation. Specifically, the hardware controller 806 causes the ageroll system 814 to age roll, or “hot roll,” the first chemicalformulation. Hot rolling includes heating the first chemical formulationso that it is conditioned to a different environment to be tested.Alternatively, the hardware controller 806 causes the static agingsystem 818 to condition the first chemical formulation for static aging.Static and dynamic aging tests are performed to test the long-termperformance of fluids after exposure to heat with or without shear. Ineither case, the first chemical formulation is then provided to a coolmix system 816 to cool the formulation down to a temperature specifiedby the cognitive computer 802.

The conditioned first chemical formulation is subsequently provided tothe testing system 822, where the conditioned first chemical formulationis tested to determine any number of parameters. Illustrativetests/testing parameters may include static aging; rheology; density;filtration; sag; pH; conductivity; time-dependent rheology; salinity;relative mass of one or more components (oil, water, etc.); and thelike. The scope of disclosure is not limited to these parameters,however, and the specific parameters that the testing system 822 testsare determined by the cognitive computer 802. The results of this test,like the results of most or all tests, are stored in one or more of theinformation repositories 828.

In some embodiments, after the testing system 822 has performed itstests, the first chemical formulation may be re-conditioned by one ormore of the components 814, 816, 818 as the cognitive computer 802 deemssuitable. Alternatively or in addition, in situations where timeconstraints are less stringent, the first chemical formulation mayundergo additional testing by the special testing system 820. Testsperformed by the special testing system 820 are generally those teststhat were not performed by the testing system 822 due to timeconstraints but that the cognitive computer 802 determines may haveadditional value—for example, determining corrosion, lubricity,long-term sag testing, toxicity, etc. The specific parameters tested inthe testing system 822 and the special testing system 820 are determinedby the cognitive computer 802. The results of the special testingperformed by the system 820 are stored in one or more informationrepositories 828. The results also are provided to the hardwarecontroller 806 and/or cognitive computer 802 to help evaluate theperformance of the mixing system 810. In some embodiments, after testingis performed by the testing system 822 or the special testing system820, no additional work is done on the first chemical formulation andthe equipment used to perform the chemistry experiment are cleaned bythe cleaning system 826 (e.g., using robotic arms controlled by thecognitive computer 802) and waste is appropriately disposed by thedisposal system 824 (e.g., again using robotic arms or similartechnology, controlled by the cognitive computer 802).

The cognitive computer 802 evaluates all experimental data collected andstored in the information repositories 828. Such evaluation may includethe use of any number of resources, including models, simulations,equations/formulas, and the like so that the cognitive computer 802 maylearn as much information as possible from the experimentation on thefirst chemical formulation. Because the cognitive computer 802 is aneurosynaptic machine, it performs such analyses intelligently,probabilistically and with minimal or no human assistance. The cognitivecomputer 802 learns from the experiment(s) and tests performed on thefirst chemical formulation. Based on this learning, the cognitivecomputer 802 may take any of a variety of actions. In some embodiments,the cognitive computer 802 uses what it learned when experimenting withthe first chemical formulation to design a second chemical formulation,and the experimentation process described above is repeated for thesecond chemical formulation. Because it is designed with the benefit oftesting results on the first chemical formulation, the second chemicalformulation typically will more closely achieve the target(s) and willbetter honor the constraint(s). For example, if the target(s) and/orconstraint(s) are objective, specific parameters, the second chemicalformulation may more closely meet such parameters than the firstchemical formulation. If the target(s) and/or constraint(s) are moresubjective in nature, the second chemical formulation will still moreclosely meet such subjective goals (as determined, e.g., by the user ofthe cognitive computer) than the first chemical formulation.

Alternatively or in addition, in some embodiments the cognitive computer802 generates a field implementation recommendation. This recommendationspecifies a plan under which the first chemical formulation (or avariant thereof) is implemented in a field scenario—for example, in adrilling operation. The recommendation, along with all pertinent data,may be presented to a user or other cognitive computer via, e.g., thedata reporting and visualization system 830. The scope of disclosure isnot limited to providing any particular types of information via thedata reporting and visualization system 830, however, and the system 830may be used to provide users and/or other cognitive computers with anyand all types of information described herein.

If a human user or other cognitive computer determines that the fieldimplementation recommendation should be approved, or if the cognitivecomputer 802 unilaterally determines that it will execute the fieldimplementation recommendation, then the field implementation system 832is used to execute the recommendation. For instance, in the case of afirst chemical formulation that is a drilling fluid additive, thecognitive computer 802 may cause the field implementation system 832 tomodify the drilling fluid in a drilling well with the additive. Thefield implementation system 832 gathers data pertaining to theimplementation and records the data to the information repositories 828.Such data may include, for instance, test results associated with thefield implementation operation. The cognitive computer 802 issubsequently able to access such information from the informationrepositories 828 when designing the second chemical formulation. Thefield implementation system 832 is generally any hardware, software,and/or human personnel that may be used to implement a particularchemical formulation in a “real-life” environment.

In some embodiments, the cognitive computer 802 may play a more passiverole and may keep unilateral actions to a minimum. In such embodiments,the cognitive computer 802 may, for example, provide the user or othercognitive computers with one or more proposed courses of action afterexperimentation and analysis of the first chemical formulation iscomplete. Thus, for instance, the cognitive computer 802 may offer theuser or other cognitive computer the option of designing a secondchemical formulation, generating a field implementation recommendation,or pursuing some other course of action. In such cases, the cognitivecomputer 802 would act only after the user or other cognitive computerhas selected one of the proposed courses of action.

FIG. 9 is a flow diagram of a method 900 used to produce and enhancechemical formulations. The method 900 begins with the cognitive computerusing target(s), constraint(s) and resources to design a chemicalformulation (step 902). The cognitive computer procures chemicals andother materials necessary to produce and test the chemical formulation(step 904). The computer identifies these chemicals and materials usingthe vast resources to which it has access in addition to the cognitivecomputer's own learning from training and prior experiences. The method900 then comprises the cognitive computer combining the chemicals and/ormaterials in accordance with its design to produce the chemicalformulation (step 906). The cognitive computer tests the chemicalformulation to determine various parameters associated with thatformulation (step 908). Non-limiting examples of such tested parametersare given above and may include rheology, density, filtration, sag, pH,physical parameters, conductivity, and the like. The cognitive computersubsequently conditions the chemical formulation and then re-tests theconditioned formulation to determine various parameters—for instance,those parameters determined during the testing in step 908 (step 910).Such conditioning may include, without limitation, hot-rolling, staticaging and cool mixing. The cognitive computer may perform special,additional testing as necessary—for example, to identify corrosion,lubricity or toxicity (step 912).

Once experimentation is complete, the cognitive computer cleans theexperimentation equipment and disposes of waste—for instance, throughthe use of robotic arms, by issuing commands to a human assistant, or acombination thereof (step 914). The results of all testing mentionedabove may be stored for future use in one or more informationrepositories (e.g., as resources) (step 916). The cognitive computerthen analyzes the testing results using its resources, prior trainingand learning, and any and all other information to which it may haveaccess (step 918). This analysis, as with all or nearly all of thecognitive computer's actions, is performed probabilistically,intelligently and with minimal or no human assistance.

The cognitive computer subsequently reports its analysis to a user orother cognitive computer (e.g., via an interface) (step 920). Suchreporting may take the form of, for instance, one or more proposedcourses of action that include the option of designing and testing a newchemical formulation based on the experimentation and testing justperformed, or that include a field implementation recommendation such asthat described above. As part of this reporting, the cognitive computermay provide arguments supporting its proposed course(s) of action. Itmay also discuss these items with the user or other cognitivecomputer—for example, in a question-and-answer format or a debateformat. The cognitive computer may unilaterally or upon commandimplement one or more proposed courses of action, such as a fieldimplementation recommendation (step 922). The cognitive computer thenobtains and analyzes results of the implemented course(s) of action(e.g., the field implementation) and adds such results to one or moreinformation repositories (step 924). Because the disclosed design andexperimentation process is iterative, the method then may begin again atstep 902. The foregoing steps may be modified in any suitable way,including the addition, deletion or rearrangement of steps.

Numerous other variations and modifications will become apparent tothose skilled in the art once the above disclosure is fully appreciated.It is intended that the following claims be interpreted to embrace allsuch variations, modifications and equivalents. In addition, the term“or” should be interpreted in an inclusive sense.

At least some embodiments are directed to a cognitive computing systemfor producing chemical formulations, comprising: neurosynapticprocessing logic; and one or more information repositories accessible tothe neurosynaptic processing logic, said one or more repositoriesstoring resources, wherein the neurosynaptic processing logic determinesa first chemical formulation to achieve a target and to satisfy one ormore constraints, produces and tests said first chemical formulation,and analyzes the results of the test using said resources to determine asecond chemical formulation, wherein the second chemical formulationmore closely achieves the target and satisfies the one or moreconstraints than the first chemical formulation. These embodiments maybe supplemented using one or more of the following concepts, in anyorder and in any combination: wherein, to produce the first chemicalformulation, the neurosynaptic processing logic causes a chemicalproduction system to procure one or more chemicals and to combine theone or more chemicals; wherein the neurosynaptic processing logic causesthe chemical production system to combine the one or more chemicals in ashear, time- and temperature-controlled environment; wherein theneurosynaptic processing logic tests the first chemical formulation todetermine one or more parameters selected from the group consisting of:rheology, density, filtration, sag, pH, conductivity, and emulsionstability; wherein the neurosynaptic processing logic causes a chemicalproduction system to condition the first chemical formulation; whereinthe chemical production system conditions the first chemical formulationto produce a conditioned first chemical formulation by hot rolling saidfirst chemical formulation, static aging the first chemical formulation,cool mixing the first chemical formulation, or a combination thereof;wherein the neurosynaptic processing logic tests the conditioned firstchemical formulation to determine one or more parameters selected fromthe group consisting of: rheology, density, filtration, sag, pH,conductivity, and emulsion stability; wherein the neurosynapticprocessing logic tests the conditioned first chemical formulation todetermine one or more additional parameters selected from the groupconsisting of: corrosion, lubricity and toxicity; wherein theneurosynaptic processing logic determines said second chemicalformulation based on results of said tests; wherein, based on saidanalysis, the neurosynaptic processing logic provides a fieldimplementation recommendation via an output interface; wherein theneurosynaptic processing logic provides and responds to arguments aboutsaid recommendation via said output interface; wherein the neurosynapticprocessing logic unilaterally or upon command implements the fieldimplementation recommendation; wherein the neurosynaptic processinglogic performs a test after implementing said recommendation and storesresults of said test in said one or more information repositories as aresource; wherein the neurosynaptic processing logic determines saidsecond chemical formulation based on results of said tests.

At least some embodiments are directed to a cognitive computing systemfor producing chemical formulations, comprising: neurosynapticprocessing logic including multiple electronic neurons operating inparallel; input and output interfaces coupled to the neurosynapticprocessing logic; and one or more information repositories accessible tothe neurosynaptic processing logic and comprising resources, wherein,via the input and output interfaces, the neurosynaptic processing logic:procures one or more materials needed to produce a chemical formulation;combines the one or more materials to produce said chemical formulation;performs a first test to determine multiple parameters of the chemicalformulation and stores results of said first test in the one or moreinformation repositories; conditions the chemical formulation to producea conditioned chemical formulation; performs one or more additionaltests to determine a plurality of parameters of the conditioned chemicalformulation and stores results of said one or more additional tests inthe one or more information repositories; and analyzes the results ofthe first test and the one or more additional tests to produce aproposed course of action. These embodiments may be supplemented usingone or more of the following concepts, in any order and in anycombination: wherein the proposed course of action is selected from thegroup consisting of: modification of the chemical formulation to producea next chemical formulation; and field implementation of the chemicalformulation; wherein the neurosynaptic processing logic uses results ofsaid field implementation to determine said next chemical formulation.

At least some embodiments are directed to a method for producingchemical formulations, comprising: using a neurosynaptic processinglogic to combine one or more materials to produce a first chemicalformulation; using the neurosynaptic processing logic to test the firstchemical formulation and to analyze results of said test; and using theneurosynaptic processing logic to probabilistically produce a secondchemical formulation based on said results, wherein the second chemicalformulation more closely achieves a predetermined target than the firstchemical formulation. These embodiments may be supplemented using one ormore of the following concepts, in any order and in any combination:wherein the neurosynaptic processing logic performs said using stepswithout human input; wherein the neurosynaptic processing logic performsa field implementation of said first chemical formulation and usesresults of said field implementation to produce the second chemicalformulation.

1. A cognitive computing system for producing chemical formulations,comprising: neurosynaptic processing logic; and one or more informationrepositories accessible to the neurosynaptic processing logic, said oneor more repositories storing resources, wherein the neurosynapticprocessing logic determines a first chemical formulation to achieve atarget and to satisfy one or more constraints, produces and tests saidfirst chemical formulation, and analyzes the results of the test usingsaid resources to determine a second chemical formulation, wherein thesecond chemical formulation more closely achieves the target andsatisfies the one or more constraints than the first chemicalformulation.
 2. The cognitive computing system of claim 1, wherein, toproduce the first chemical formulation, the neurosynaptic processinglogic causes a chemical production system to procure one or morechemicals and to combine the one or more chemicals.
 3. The cognitivecomputing system of claim 2, wherein the neurosynaptic processing logiccauses the chemical production system to combine the one or morechemicals in a shear, time- and temperature-controlled environment. 4.The cognitive computing system of claim 1, wherein the neurosynapticprocessing logic tests the first chemical formulation to determine oneor more parameters selected from the group consisting of: rheology,density, filtration, sag, pH, conductivity, emulsion stability,time-dependent rheology, salinity, and relative mass of one or morecomponents of the first chemical formulation.
 5. The cognitive computingsystem of claim 1, wherein the neurosynaptic processing logic causes achemical production system to condition the first chemical formulation.6. The cognitive computing system of claim 5, wherein the chemicalproduction system conditions the first chemical formulation to produce aconditioned first chemical formulation by hot rolling said firstchemical formulation, preparing the first chemical formulation forstatic aging, cool mixing the first chemical formulation, or acombination thereof.
 7. The cognitive computing system of claim 6,wherein the neurosynaptic processing logic tests the conditioned firstchemical formulation to determine one or more parameters selected fromthe group consisting of: rheology, density, filtration, sag, pH,conductivity, emulsion stability, time-dependent rheology, salinity, andrelative mass of one or more components of the conditioned firstchemical formulation.
 8. The cognitive computing system of claim 7,wherein the neurosynaptic processing logic tests the conditioned firstchemical formulation to determine one or more additional parametersselected from the group consisting of: corrosion, lubricity andtoxicity.
 9. The cognitive computing system of claim 8, wherein theneurosynaptic processing logic determines said second chemicalformulation based on results of said tests.
 10. The cognitive computingsystem of claim 1, wherein, based on said analysis, the neurosynapticprocessing logic provides a field implementation recommendation via anoutput interface.
 11. The cognitive computing system of claim 10,wherein the neurosynaptic processing logic provides and responds toarguments about said recommendation via said output interface.
 12. Thecognitive computing system of claim 10, wherein the neurosynapticprocessing to logic unilaterally or upon command implements the fieldimplementation recommendation.
 13. The cognitive computing system ofclaim 12, wherein the neurosynaptic processing logic performs a testafter implementing said recommendation and stores results of said testin said one or more information repositories as a resource.
 14. Thecognitive computing system of claim 13, wherein the neurosynapticprocessing logic determines said second chemical formulation based onresults of said tests.
 15. A cognitive computing system for producingchemical formulations, comprising: neurosynaptic processing logicincluding multiple electronic neurons operating in parallel; input andoutput interfaces coupled to the neurosynaptic processing logic; and oneor more information repositories accessible to the neurosynapticprocessing logic and comprising resources, wherein, via the input andoutput interfaces, the neurosynaptic processing logic: procures one ormore materials needed to produce a chemical formulation; combines theone or more materials to produce said chemical formulation; performs afirst test to determine multiple parameters of the chemical formulationand stores results of said first test in the one or more informationrepositories; conditions the chemical formulation to produce aconditioned chemical formulation; performs one or more additional teststo determine a plurality of parameters of the conditioned chemicalformulation and stores results of said one or more additional tests inthe one or more information repositories; and analyzes the results ofthe first test and the one or more additional tests to produce aproposed course of action.
 16. The cognitive computing system of claim15, wherein the proposed course of action is selected from the groupconsisting of: modification of the chemical formulation to produce anext chemical formulation; and field implementation of the chemicalformulation.
 17. The cognitive computing system of claim 16, wherein theneurosynaptic processing logic uses results of said field implementationto determine said next chemical formulation.
 18. A method for producingchemical formulations, comprising: using a neurosynaptic processinglogic to combine one or more materials to produce a first chemicalformulation; using the neurosynaptic processing logic to test the firstchemical formulation and to analyze results of said test; and using theneurosynaptic processing logic to probabilistically produce a secondchemical formulation based on said results, wherein the second chemicalformulation more closely achieves a predetermined target than the firstchemical formulation.
 19. The method of claim 18, wherein theneurosynaptic processing logic performs said using steps without humaninput.
 20. The method of claim 18, wherein the neurosynaptic processinglogic performs a field implementation of said first chemical formulationand uses results of said field implementation to produce the secondchemical formulation.